Is AI-Driven Innovation Outpacing Human Ingenuity?

This gap has proven to be fertile ground for debating the future of corporate innovation in general and the potential of AI-driven innovation specifically.

As with any emerging technology, there are many misconceptions about using AI for innovation and innovation management, ranging from overinflated expectations to underestimated potential. We’re awash in studies and stories about Generative AI, in particular—how it’s already risen to human-level creativity, rivaling MBA students in idea generation, and promising trillions of dollars in productivity gains.

Meanwhile, detractors argue that AI, with its logic-driven algorithms, lacks the flair and intuition inherent to human creativity. We’re left with the pivotal question, the artificial elephant in the room: Is AI-driven innovation outpacing human ingenuity?

Why AI-driven Innovation?

Let’s first discuss why we’re talking about AI-driven innovation in the first place. Why is a shift in how companies innovate imperative, if not inevitable?

Here is what we know to be true:

• The pace of change and technology is accelerating. Rising economic uncertainty and market volatility are felt acutely by companies across industries and geographies. Without intervention or adaptation, 80% of existing business models are at risk.
• Companies are reacting to this heightened risk by ramping up investments in innovation. And yet, many do not see the tangible results they expect from this spending. Over the past five years, US companies have sunk $1 trillion in innovation projects that led nowhere. A staggering 95% of new products and services fail.
• The reasons for these diminishing returns on innovation investments are often preventable internal obstacles like failing to spot and respond to meaningful signals of change, hitting creative roadblocks in ideation, and expending high-value resources—time, money, expertise—on low-value endeavors.

It is clear that the traditional ways of innovating, which have tended to rely solely on collective creativity, are bound by inherent limitations. These conditions have created the perfect opportunity to shift to a radically more effective model of innovation. Essentially, we need a new way to innovate.

What is AI-driven innovation?

Enter: AI-driven innovation. A new model of innovation that leverages AI technologies to enhance and streamline traditional innovation practices, transforming the way companies protect and grow their market share.

The most revolutionary change that’s occurring right now is that algorithms are better at doing the things we care about in nearly every domain of our lives—including innovation. And there’s mounting evidence to support this claim:

• In my research on machine-learning-based approaches to continuous foresight, the benefits of AI in trend and technology monitoring are irrefutable. Innovation teams using our AI-based method for automated signal selection are significantly better at separating noise from news—45% of the signals identified were both novel and relevant.
• From ITONICS’ experience running an ideation challenge with a multinational, multi-billion-dollar corporation, we saw that ideas generated by AI outperformed those submitted by humans. Despite the challenge being open to more than 10,000 innovators, two out of the three winning ideas were the products of Generative AI (GenAI).
• And we’ve seen time and time again the impact of connecting the dots across all innovation initiatives—across teams and from strategy through to execution—to ensure a balanced and aligned portfolio. Companies that streamline portfolio management with machine intelligence achieve massive cost and time savings.

Indeed, there are already many successful examples of AI-driven innovation. One of the most well-funded research areas in this regard is AI-enabled drug discovery. Bringing a new drug to market can take years with a price tag in the billions of dollars. But AI is creating unprecedented efficiencies at nearly every stage of the drug discovery process, from molecular simulations to forecasting drug effectiveness. With startups like Iktos Robotics already well on the way toward fully automated end-to-end drug discovery, the rest of the industry will soon follow.

Then there are the groundbreaking applications of GenAI, which are revolutionizing a host of creative fields. Based on patterns and knowledge learned from existing data, these AI models can autonomously generate original ideas, enhance product design processes, and inspire creative breakthroughs that push the boundaries of traditional methodologies. Nearly a third of top innovators are already deploying GenAI at scale in their innovation and R&D functions.

We are decisively entering a new era of augmented intelligence where a company’s capacity for innovation is less about harnessing the “wisdom of the crowd” and more about strategically deploying AI to channel that wisdom, unlock new opportunities, and drive systematic growth.

The Future of Corporate Innovation

Here’s what over a decade of working at the nexus of artificial intelligence and innovation has taught me: The truly game-changing potential of AI comes from it being successfully implemented and seamlessly integrated throughout the full innovation journey. AI is not a silver bullet or a mere standalone solution but a catalyst for magnifying human ingenuity, improving decision-making processes, and delivering efficiency, speed, and scale.

The future of corporate innovation doesn’;t lie in choosing between human and machine; it’s about harnessing the combined, synergistic power of both. I believe this new way of innovating is an inevitability, but as with the diffusion of any new technology, it will likely follow the adoption curve:

• Laggards: Carry on with innovation as usual, relying on collective creativity alone and the use of traditional, often fragmented methods.
• Majority: Apply readily available AI tools for isolated use cases at specific steps in the innovation process.
• Innovators: Embrace the transformative power of AI, seamlessly integrating human and machine capabilities throughout end-to-end innovation.

Really, there should be only one choice, operating in the field of innovation as we do: we must be innovators. And that means spearheading the transition toward AI-driven innovation by intertwining human ingenuity with machine intelligence at the very frontier of capabilities.

With all of this in mind, the real question isn’t if AI-driven innovation will outpace human ingenuity—by some accounts, it has already surpassed the average human. The real question is: how do we evolve alongside these advances?

The onus is on innovation professionals to embrace augmented intelligence, infusing it into the core of their innovation processes. And where we once were problem solvers, we must master the art of problem discovery. The required skills of tomorrow’s innovators will no longer rely on unbound creativity but on asking provocative questions, emphasizing with customers, and providing a strategic vision of their preferred future.

Ultimately, I believe that the spark of innovation will still lie in human ingenuity, collaboration, and diverse perspectives. But igniting that spark will require an AI-powered engine capable of unlocking unprecedented levels of efficiency, precision, and adaptability in innovation, driving the sorts of breakthroughs that redefine industries and challenge the status quo.